Xinjiang Agricultural Sciences ›› 2024, Vol. 61 ›› Issue (4): 845-851.DOI: 10.6048/j.issn.1001-4330.2024.04.007
• Crop Genetics and Breeding·Germplasm Resources·Molecular Genetics·Physiology and Biochemistry • Previous Articles Next Articles
ZHANG Lei1,2(), YAO Mengyao2,3, LIU Zhigang2, LI Juan2, YANG Yang2, CAI Darun2, CHEN Guo2, LI Bo2, LI Xiaorong2, CHEN Xunji2, ZHAI Yunlong1()
Received:
2023-09-11
Online:
2024-04-20
Published:
2024-05-31
Correspondence author:
ZHAI Yunlong
Supported by:
张磊1,2(), 姚梦瑶2,3, 刘志刚2, 李娟2, 杨洋2, 蔡大润2, 陈果2, 李波2, 李晓荣2, 陈勋基2, 翟云龙1()
通讯作者:
翟云龙
作者简介:
张磊(1999-),男,甘肃武威人,硕士研究生,研究方向为玉米遗传育种,(E-mail)1822835613@qq.com
基金资助:
CLC Number:
ZHANG Lei, YAO Mengyao, LIU Zhigang, LI Juan, YANG Yang, CAI Darun, CHEN Guo, LI Bo, LI Xiaorong, CHEN Xunji, ZHAI Yunlong. Research of maize yield estimation based on unmanned aerial vehicle multispectral NDVI[J]. Xinjiang Agricultural Sciences, 2024, 61(4): 845-851.
张磊, 姚梦瑶, 刘志刚, 李娟, 杨洋, 蔡大润, 陈果, 李波, 李晓荣, 陈勋基, 翟云龙. 基于无人机多光谱NDVI值估测玉米产量[J]. 新疆农业科学, 2024, 61(4): 845-851.
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玉米种质名称 Corn germplasm name | NDVI | 实测产量 Measured yield (kg/hm2) |
---|---|---|
20HN009×16F | 0.746 771 | 6 309.52 |
20HN064×DB614 | 0.781 470 | 7 261.90 |
20HN068×DB614 | 0.634 637 | 4 404.76 |
20HN069×DB614 | 0.663 515 | 5 595.23 |
20HN070×DB614 | 0.792 066 | 11 666.66 |
20HN084×DB614 | 0.672 930 | 6 071.42 |
20HN086×DB614 | 0.781 432 | 9 880.95 |
20HN096×DB614 | 0.565 060 | 5 476.19 |
20HN102×DB614 | 0.603 867 | 5 833.33 |
20HN104×DB614 | 0.743 107 | 9 523.80 |
20HN142×DB614 | 0.728 815 | 7 142.85 |
20HN152×DB614 | 0.748 561 | 6 666.66 |
20HN094×1487F | 0.719 880 | 10 476.19 |
20HN095×1487F | 0.794 545 | 11 071.42 |
20HN098×1487F | 0.793 120 | 10 833.34 |
20HN107×1487F | 0.740 062 | 9 523.80 |
20HN153×K487F | 0.728 140 | 9 880.95 |
20HN326×A55 | 0.756 129 | 7 023.80 |
Tab.1 18 corn material yields correspond to NDVI tables
玉米种质名称 Corn germplasm name | NDVI | 实测产量 Measured yield (kg/hm2) |
---|---|---|
20HN009×16F | 0.746 771 | 6 309.52 |
20HN064×DB614 | 0.781 470 | 7 261.90 |
20HN068×DB614 | 0.634 637 | 4 404.76 |
20HN069×DB614 | 0.663 515 | 5 595.23 |
20HN070×DB614 | 0.792 066 | 11 666.66 |
20HN084×DB614 | 0.672 930 | 6 071.42 |
20HN086×DB614 | 0.781 432 | 9 880.95 |
20HN096×DB614 | 0.565 060 | 5 476.19 |
20HN102×DB614 | 0.603 867 | 5 833.33 |
20HN104×DB614 | 0.743 107 | 9 523.80 |
20HN142×DB614 | 0.728 815 | 7 142.85 |
20HN152×DB614 | 0.748 561 | 6 666.66 |
20HN094×1487F | 0.719 880 | 10 476.19 |
20HN095×1487F | 0.794 545 | 11 071.42 |
20HN098×1487F | 0.793 120 | 10 833.34 |
20HN107×1487F | 0.740 062 | 9 523.80 |
20HN153×K487F | 0.728 140 | 9 880.95 |
20HN326×A55 | 0.756 129 | 7 023.80 |
名称 Name | 无人机 Unmanned aerial vehicle | 多光谱相机 Multispectral camera |
---|---|---|
型号Model | EcoDrone® UAS-8 | MicaSense Red Edge-MTM |
尺寸Size | 1 632 mm × 1 632 mm × 650 mm(螺旋桨、GPS支架均展开) | 94 mm × 63 mm× 46 mm |
重量 Weight | 10 kg(最大载荷6 kg) | 170 g (包括DLS) |
焦距 Focal length | 35 mm | |
最大分辨率 Maximum resolution | 1 280 × 960 | |
光谱带 Spectval band | R、G、B、NIR、Red edge | |
作业时间 Operation time | 30 min | 30 min |
最大可承受风速 Maximum tolerable wind speed | 10 m/s(5级风可安全飞行)、 瞬间可承受13 m/s (6级风) | 10 m/s(5级风可安全飞行)、 瞬间可承受13 m/s (6级风) |
Tab.2 Parameter of UAV remote sensing platform
名称 Name | 无人机 Unmanned aerial vehicle | 多光谱相机 Multispectral camera |
---|---|---|
型号Model | EcoDrone® UAS-8 | MicaSense Red Edge-MTM |
尺寸Size | 1 632 mm × 1 632 mm × 650 mm(螺旋桨、GPS支架均展开) | 94 mm × 63 mm× 46 mm |
重量 Weight | 10 kg(最大载荷6 kg) | 170 g (包括DLS) |
焦距 Focal length | 35 mm | |
最大分辨率 Maximum resolution | 1 280 × 960 | |
光谱带 Spectval band | R、G、B、NIR、Red edge | |
作业时间 Operation time | 30 min | 30 min |
最大可承受风速 Maximum tolerable wind speed | 10 m/s(5级风可安全飞行)、 瞬间可承受13 m/s (6级风) | 10 m/s(5级风可安全飞行)、 瞬间可承受13 m/s (6级风) |
影像号顺序 Image number order | 波段 Band (nm) | 波段类型 Band type | 波段宽度 Band width (nm) |
---|---|---|---|
1 | 475 | Blue | 20 |
2 | 560 | Green | 20 |
3 | 668 | Red | 10 |
4 | 840 | NIR | 40 |
5 | 717 | Red Edge | 10 |
Tab.3 The order of images of multispectral cameras and the corresponding band type and width
影像号顺序 Image number order | 波段 Band (nm) | 波段类型 Band type | 波段宽度 Band width (nm) |
---|---|---|---|
1 | 475 | Blue | 20 |
2 | 560 | Green | 20 |
3 | 668 | Red | 10 |
4 | 840 | NIR | 40 |
5 | 717 | Red Edge | 10 |
估产模型Estimation Model | 表达式Expression | R2 |
---|---|---|
二次函数Quadratic Function | Y1 = 103 130 X2-117 963 X+39 003 | 0.562 |
正反比函数Inverse Proportional Function | Y2 = 2 840.5 X/(1-X) | 0.495 |
线性函数 Linear Function | Y3 = 24 458 X-9 621 | 0.521 |
幂函数Power Function | Y4 = 23 412-10 998/ X | 0.489 |
Tab.4 Four production estimation models
估产模型Estimation Model | 表达式Expression | R2 |
---|---|---|
二次函数Quadratic Function | Y1 = 103 130 X2-117 963 X+39 003 | 0.562 |
正反比函数Inverse Proportional Function | Y2 = 2 840.5 X/(1-X) | 0.495 |
线性函数 Linear Function | Y3 = 24 458 X-9 621 | 0.521 |
幂函数Power Function | Y4 = 23 412-10 998/ X | 0.489 |
估产模型 Estimation model | 绝对误差Absolute error(kg/hm2 ) | 相对误差Relative error(%) | ||||
---|---|---|---|---|---|---|
最大值 Max | 最小值 Min | 均值 Mean | 最大值 Max | 最小值 Min | 均值 Mean | |
二次函数 Quadratic Functions | 2 947.50 | 83.00 | 1 216.86 | 28.13 | 0.84 | 15.79 |
正反比函数 Inverse Proportional Function | 3 176.39 | 5.94 | 1 251.98 | 30.32 | 0.10 | 16.63 |
线性函数 Linear functions | 2 489.75 | 389.07 | 1 419.35 | 23.76 | 3.93 | 19.02 |
幂函数 Power function | 2 374.54 | 543.65 | 1 493.19 | 37.63 | 5.50 | 20.14 |
Tab.5 Accuracy analysis of different yield forecast models
估产模型 Estimation model | 绝对误差Absolute error(kg/hm2 ) | 相对误差Relative error(%) | ||||
---|---|---|---|---|---|---|
最大值 Max | 最小值 Min | 均值 Mean | 最大值 Max | 最小值 Min | 均值 Mean | |
二次函数 Quadratic Functions | 2 947.50 | 83.00 | 1 216.86 | 28.13 | 0.84 | 15.79 |
正反比函数 Inverse Proportional Function | 3 176.39 | 5.94 | 1 251.98 | 30.32 | 0.10 | 16.63 |
线性函数 Linear functions | 2 489.75 | 389.07 | 1 419.35 | 23.76 | 3.93 | 19.02 |
幂函数 Power function | 2 374.54 | 543.65 | 1 493.19 | 37.63 | 5.50 | 20.14 |
估产模型 Estimation model | ME | RMSE (kg/hm2 ) | SD |
---|---|---|---|
二次函数 Quadratic Functions | 1 374.70 | 443.12 | 1 716.21 |
正反比函数 Inverse Proportional Function | 1 791.36 | 536.10 | 2 274.49 |
线性函数 Linear functions | 1 285.96 | 390.41 | 1 656.41 |
幂函数 power function | 1 227.69 | 539.59 | 2 224.78 |
Tab.6 Statistical analysis of the error between actual output and predicted output
估产模型 Estimation model | ME | RMSE (kg/hm2 ) | SD |
---|---|---|---|
二次函数 Quadratic Functions | 1 374.70 | 443.12 | 1 716.21 |
正反比函数 Inverse Proportional Function | 1 791.36 | 536.10 | 2 274.49 |
线性函数 Linear functions | 1 285.96 | 390.41 | 1 656.41 |
幂函数 power function | 1 227.69 | 539.59 | 2 224.78 |
[1] | 李学国. 基于无人机遥感光谱图像的小麦玉米长势精准监测研究[D]. 泰安: 山东农业大学, 2019. |
LI Xueguo. Study on Precise Monitoring of Wheat and Corn Growth Based on Remote Sensing Image of Unmanned Aerial Vehicle[D]. Taian: Shandong Agricultural University, 2019. | |
[2] | 陈怀亮, 李颖, 张红卫. 农作物长势遥感监测业务化应用与研究进展[J]. 气象与环境科学, 2015, 38(1): 95-102. |
CHEN Huailiang, LI Ying, ZHANG Hongwei. Operational application and research review of crop growth monitoring with remote sensing[J]. Meteorological and Environmental Sciences, 2015, 38(1): 95-102. | |
[3] | 高林, 杨贵军, 王宝山, 等. 基于无人机遥感影像的大豆叶面积指数反演研究[J]. 中国生态农业学报, 2015, 23(7): 868-876. |
GAO Lin, YANG Guijun, WANG Baoshan, et al. Soybean leaf area index retrieval with UAV(unmanned aerial vehicle) remote sensing imagery[J]. Chinese Journal of Eco-Agriculture, 2015, 23(7): 868-876. | |
[4] | Han X Z, Thomasson J A, Bagnall G C, et al. Measurement and calibration of plant-height from fixed-wing UAV images[J]. Sensors, 2018, 18(12): 4092. |
[5] | Shafian S, Rajan N, Schnell R, et al. Using a fixed wing UAV remote sensing system for Sorghum Crop Phenotyping [A]// 2016: B53H-B612H. |
[6] | Geipel J, Link J, Claupein W. Combined spectral and spatial modeling of corn yield based on aerial images and crop surface models acquired with an unmanned aircraft system[J]. Remote Sensing, 2014, 6(11): 10335-10355. |
[7] | Magney T S, Eitel J U H, Huggins D R, et al. Proximal NDVI derived phenology improves in-season predictions of wheat quantity and quality[J]. Agricultural and Forest Meteorology, 2016, 217: 46-60. |
[8] | Foster A J, Kakani V G, Mosali J. Estimation of bioenergy crop yield and N status by hyperspectral canopy reflectance and partial least square regression[J]. Precision Agriculture, 2017, 18(2): 192-209. |
[22] | 贺佳, 王来刚, 郭燕, 等. 基于无人机多光谱遥感的玉米LAI估算研究[J]. 农业大数据学报, 2021, 3(4): 20-28. |
HE Jia, WANG Laigang, GUO Yan, et al. Research on maize LAI estimation based on UAV multispectral remote sensing[J]. Journal of Agricultural Big Data, 2021, 3(4): 20-28. | |
[9] | Samborski S M, Gozdowski D, Walsh O S, et al. Winter wheat genotype effect on canopy reflectance: implications for using NDVI for In-season nitrogen topdressing recommendations[J]. Agronomy Journal, 2015, 107(6): 2097-2106. |
[10] | Rutkoski J, Poland J, Mondal S, et al. Canopy temperature and vegetation indices from high-throughput phenotyping improve accuracy of pedigree and genomic selection for grain yield in wheat[J]. G3 Genes|Genomes|Genetics, 2016, 6(9): 2799-2808. |
[11] | Kumar S, Röder M S, Singh R P, et al. Mapping of spot blotch disease resistance using NDVI as a substitute to visual observation in wheat (Triticum aestivum L.)[J]. Molecular Breeding, 2016, 36(7): 95. |
[12] | Kyratzis A, Skarlatos D, Fotopoulos V, et al. Investigating correlation among NDVI index derived by unmanned aerial vehicle photography and grain yield under late drought stress conditions[J]. Procedia Environmental Sciences, 2015, 29: 225-226. |
[13] | Babar M A, Reynolds M P, van Ginkel M, et al. Spectral reflectance indices as a potential indirect selection criteria for wheat yield under irrigation[J]. Crop Science, 2006, 46(2): 578-588. |
[14] | 陈晨. 基于无人机图像的小麦生物量与产量的估测研究[D]. 扬州: 扬州大学, 2019. |
CHEN Chen. Estimation of Wheat Biomass and Yield Based on UAV Images[D]. Yangzhou: Yangzhou University, 2019. | |
[15] | Chen Y, Donohue R J, McVicar T R, et al. Nationwide crop yield estimation based on photosynthesis and meteorological stress indices[J]. Agricultural and Forest Meteorology, 2020, 284: 107872. |
[16] | Chakrabarti S, Bongiovanni T, Judge J, et al. Assimilation of SMOS soil moisture for quantifying drought impacts on crop yield in agricultural regions[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(9): 3867-3879. |
[17] | Sakamoto T. Incorporating environmental variables into a MODIS-based crop yield estimation method for United States corn and soybeans through the use of a random forest regression algorithm[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 160: 208-228. |
[18] | 田明璐, 班松涛, 常庆瑞, 等. 基于无人机成像光谱仪数据的棉花叶绿素含量反演[J]. 农业机械学报, 2016, 47(11): 285-293. |
TIAN Minglu, BAN Songtao, CHANG Qingrui, et al. Estimation of SPAD value of cotton leaf using hyperspectral images from UAV-based imaging spectroradiometer[J]. Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(11): 285-293. | |
[19] | 孟沌超. 基于无人机可见光影像的棉花氮素和叶绿素反演方法研究[D]. 淄博: 山东理工大学, 2021. |
MENG Dunchao. Study on inversion method of cotton nitrogen and chlorophyll based on UAV visible light image[D]. Zibo: Shandong University of Technology, 2021. | |
[20] | 刘小辉. 基于无人机影像的小麦叶绿素含量及产量定量反演研究[D]. 合肥: 安徽大学, 2019. |
LIU Xiaohui. Inversion of Wheat Chlorophyll Content and Yield Based on Unmanned Aerial Vehicle Images[D]. Hefei: Anhui University, 2019. | |
[21] | 邹楠, 杨文杰, 肖春华, 等. 种植密度对玉米冠层高光谱特征的响应研究[J]. 石河子大学学报(自然科学版), 2017, 35(6): 687-692. |
ZOU Nan, YANG Wenjie, XIAO Chunhua, et al. Response of planting density to hyperspectral characteristics of maize canopy[J]. Journal of Shihezi University (Natural Science), 2017, 35(6): 687-692. |
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